Crop Biometric Maps: The Key to Prediction

[EN] The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” Thi...

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Detalles Bibliográficos
Autores: Rovira Más, Francisco|||0000-0002-2589-9281, Saiz Rubio, Verónica|||0000-0003-4188-3666
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/64344
Acceso en línea:https://riunet.upv.es/handle/10251/64344
Access Level:acceso abierto
Palabra clave:precision farming
global positioning
yield prediction
crop monitoring
vineyard management
precision viticulture
agricultural robotics
information technology
INGENIERIA AGROFORESTAL
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spelling Crop Biometric Maps: The Key to PredictionRovira Más, Francisco|||0000-0002-2589-9281Saiz Rubio, Verónica|||0000-0003-4188-3666precision farmingglobal positioningyield predictioncrop monitoringvineyard managementprecision viticultureagricultural roboticsinformation technologyINGENIERIA AGROFORESTAL[EN] The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.The authors would like to express their gratitude to Edmund Optics, Inc. for supporting the ideas developed in this article with the 2011 Research and Innovation Award, as well as to the Farming by Satellite 2012 Prize sponsored by Claas, Bayer CropScience, and the European GNSS Agency (GSA).MDPIDepartamento de Ingeniería Rural y AgroalimentariaEscuela Técnica Superior de Ingeniería Agronómica y del Medio NaturalGrupo de Mecanización y Tecnología AgrariaEdmund Optics, Inc.Bayer CropScienceEuropean GNSS AgencyRepositorio Institucional de la Universitat Politècnica de València Riunet20132013-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/64344reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/643442026-06-13T07:49:27Z
dc.title.none.fl_str_mv Crop Biometric Maps: The Key to Prediction
title Crop Biometric Maps: The Key to Prediction
spellingShingle Crop Biometric Maps: The Key to Prediction
Rovira Más, Francisco|||0000-0002-2589-9281
precision farming
global positioning
yield prediction
crop monitoring
vineyard management
precision viticulture
agricultural robotics
information technology
INGENIERIA AGROFORESTAL
title_short Crop Biometric Maps: The Key to Prediction
title_full Crop Biometric Maps: The Key to Prediction
title_fullStr Crop Biometric Maps: The Key to Prediction
title_full_unstemmed Crop Biometric Maps: The Key to Prediction
title_sort Crop Biometric Maps: The Key to Prediction
dc.creator.none.fl_str_mv Rovira Más, Francisco|||0000-0002-2589-9281
Saiz Rubio, Verónica|||0000-0003-4188-3666
author Rovira Más, Francisco|||0000-0002-2589-9281
author_facet Rovira Más, Francisco|||0000-0002-2589-9281
Saiz Rubio, Verónica|||0000-0003-4188-3666
author_role author
author2 Saiz Rubio, Verónica|||0000-0003-4188-3666
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Rural y Agroalimentaria
Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural
Grupo de Mecanización y Tecnología Agraria
Edmund Optics, Inc.
Bayer CropScience
European GNSS Agency
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv precision farming
global positioning
yield prediction
crop monitoring
vineyard management
precision viticulture
agricultural robotics
information technology
INGENIERIA AGROFORESTAL
topic precision farming
global positioning
yield prediction
crop monitoring
vineyard management
precision viticulture
agricultural robotics
information technology
INGENIERIA AGROFORESTAL
description [EN] The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/64344
url https://riunet.upv.es/handle/10251/64344
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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